




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       KandinskyInpaintPipeline
						UpperCamelCase      =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						UpperCamelCase      =       [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						UpperCamelCase      =       [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase      =       False

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=MCLIPConfig(
										    numDims=self.cross_attention_dim     ,			transformerDimensions=self.text_embedder_hidden_size     ,			hidden_size=self.text_embedder_hidden_size     ,			intermediate_size=37     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			vocab_size=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """in_channels""": 9,
										    # Out channels is double in channels because predicts mean and variance
										    """out_channels""": 8,
										    """addition_embed_type""": """text_image""",
										    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
										    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
										    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
										    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
										    """layers_per_block""": 1,
										    """encoder_hid_dim""": self.text_embedder_hidden_size,
										    """encoder_hid_dim_type""": """text_image_proj""",
										    """cross_attention_dim""": self.cross_attention_dim,
										    """attention_head_dim""": 4,
										    """resnet_time_scale_shift""": """scale_shift""",
										    """class_embed_type""": None,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return {
										    "block_out_channels": [32, 64],
										    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}

						@property
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[Any]										={
										    """prompt""": """horse""",
										    """image""": init_image,
										    """mask_image""": mask,
										    """image_embeds""": image_embeds,
										    """negative_image_embeds""": negative_image_embeds,
										    """generator""": generator,
										    """height""": 64,
										    """width""": 64,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 4.0,
										    """output_type""": """np""",
										}
										return inputs

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

										__magic_name__						:						int										=image[0, -3:, -3:, -1]
										__magic_name__						:						str										=image_from_tuple[0, -3:, -3:, -1]

										print(f"image.shape {image.shape}"			)

										assert image.shape == (1, 64, 64, 3)

										__magic_name__						:						Optional[Any]										=np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]			)

										assert (
										    np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
										assert (
										    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
										), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"


						def      A__    (		self :Dict			):
										'''simple docstring'''

										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




@slow
@require_torch_gpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""			)

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

										__magic_name__						:						Dict										=KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""     ,			torch_dtype=torch.floataa			)
										__magic_name__						:						int										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)










'''simple docstring'''







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase_     						=				1_6
lowerCAmelCase_     						=				3_2







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase = 16						):






   '''simple docstring'''







   A__       =     AutoTokenizer.from_pretrained('bert-base-cased'						)
   A__       =     DatasetDict(
       {
           'train': dataset['train'].select(UpperCAmelCase						),
           'validation': dataset['train'].select(UpperCAmelCase						),
           'test': dataset['validation'],
       }						)

   def tokenize_function(UpperCAmelCase						):
      # max_length=None => use the model max length (it's actually the default)
      A__       =     tokenizer(examples['sentence1']      ,examples['sentence2']      ,truncation=UpperCAmelCase      ,max_length=UpperCAmelCase						)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      A__       =     datasets.map(
          UpperCAmelCase      ,batched=UpperCAmelCase      ,remove_columns=['idx', 'sentence1', 'sentence2']      ,)

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   A__       =     tokenized_datasets.rename_column('label'      ,'labels'						)

   def collate_fn(UpperCAmelCase						):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A__       =     128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         A__       =     16
      elif accelerator.mixed_precision != "no":
         A__       =     8
      else:
         A__       =     None

      return tokenizer.pad(
          UpperCAmelCase      ,padding='longest'      ,max_length=UpperCAmelCase      ,pad_to_multiple_of=UpperCAmelCase      ,return_tensors='pt'      ,)

   # Instantiate dataloaders.
   A__       =     DataLoader(
       tokenized_datasets['train']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=UpperCAmelCase						)
   A__       =     DataLoader(
       tokenized_datasets['validation']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=UpperCAmelCase						)

   A__       =     DataLoader(
       tokenized_datasets['test']      ,shuffle=UpperCAmelCase      ,collate_fn=UpperCAmelCase      ,batch_size=UpperCAmelCase						)

   return train_dataloader, eval_dataloader, test_dataloader







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   # Download the dataset
   A__       =     load_dataset('glue'      ,'mrpc'						)
   # Create our splits
   A__       =     StratifiedKFold(n_splits=int(args.num_folds						)						)
   # Initialize accelerator
   A__       =     Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision						)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A__       =     config['lr']
   A__       =     int(config['num_epochs']						)
   A__       =     int(config['seed']						)
   A__       =     int(config['batch_size']						)

   A__       =     evaluate.load('glue'      ,'mrpc'						)

   # If the batch size is too big we use gradient accumulation
   A__       =     1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A__       =     batch_size // MAX_GPU_BATCH_SIZE
      A__       =     MAX_GPU_BATCH_SIZE

   set_seed(UpperCAmelCase						)

   # New Code #
   # Create our folds:
   A__       =     kfold.split(np.zeros(datasets['train'].num_rows						)      ,datasets['train']['label']						)
   A__       =     []
   # Iterate over them
   for i, (train_idxs, valid_idxs) in enumerate(UpperCAmelCase						):
      A__  ,			A__  ,			A__       =     get_fold_dataloaders(
          UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      A__       =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased'      ,return_dict=UpperCAmelCase						)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      A__       =     model.to(accelerator.device						)

      # Instantiate optimizer
      A__       =     AdamW(params=model.parameters()      ,lr=UpperCAmelCase						)

      # Instantiate scheduler
      A__       =     get_linear_schedule_with_warmup(
          optimizer=UpperCAmelCase      ,num_warmup_steps=100      ,num_training_steps=(len(UpperCAmelCase						) * num_epochs) // gradient_accumulation_steps      ,)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      A__  ,			A__  ,			A__  ,			A__  ,			A__       =     accelerator.prepare(
          UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

      # Now we train the model
      for epoch in range(UpperCAmelCase						):
         model.train()
         for step, batch in enumerate(UpperCAmelCase						):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device						)
            A__       =     model(**UpperCAmelCase						)
            A__       =     outputs.loss
            A__       =     loss / gradient_accumulation_steps
            accelerator.backward(UpperCAmelCase						)
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

         model.eval()
         for step, batch in enumerate(UpperCAmelCase						):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device						)
            with torch.no_grad():
               A__       =     model(**UpperCAmelCase						)
            A__       =     outputs.logits.argmax(dim=-1						)
            A__  ,			A__       =     accelerator.gather_for_metrics((predictions, batch['labels'])						)
            metric.add_batch(
                predictions=UpperCAmelCase      ,references=UpperCAmelCase      ,)

         A__       =     metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(F"""epoch {epoch}:"""      ,UpperCAmelCase						)

      # New Code #
      # We also run predictions on the test set at the very end
      A__       =     []
      for step, batch in enumerate(UpperCAmelCase						):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device						)
         with torch.no_grad():
            A__       =     model(**UpperCAmelCase						)
         A__       =     outputs.logits
         A__  ,			A__       =     accelerator.gather_for_metrics((predictions, batch['labels'])						)
         fold_predictions.append(predictions.cpu()						)
         if i == 0:
            # We need all of the test predictions
            test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
      test_predictions.append(torch.cat(UpperCAmelCase      ,dim=0						)						)
      # We now need to release all our memory and get rid of the current model, optimizer, etc
      accelerator.free_memory()
   # New Code #
   # Finally we check the accuracy of our folded results:
   A__       =     torch.cat(UpperCAmelCase      ,dim=0						)
   A__       =     torch.stack(UpperCAmelCase      ,dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
   A__       =     metric.compute(predictions=UpperCAmelCase      ,references=UpperCAmelCase						)
   accelerator.print('Average test metrics from all folds:'      ,UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     argparse.ArgumentParser(description='Simple example of training script.'						)
   parser.add_argument(
       '--mixed_precision'      ,type=UpperCAmelCase      ,default=UpperCAmelCase      ,choices=['no', 'fp16', 'bf16', 'fp8']      ,help='Whether to use mixed precision. Choose'
       'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
       'and an Nvidia Ampere GPU.'      ,)
   parser.add_argument('--cpu'      ,action='store_true'      ,help='If passed, will train on the CPU.'						)
   # New Code #
   parser.add_argument('--num_folds'      ,type=UpperCAmelCase      ,default=3      ,help='The number of splits to perform across the dataset'						)
   A__       =     parser.parse_args()
   A__       =     {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(UpperCAmelCase      ,UpperCAmelCase						)


if __name__ == "__main__":
    main()


